1. EE 5393: Circuits, Computation and Biology
Marc D. Riedel
Assistant Professor, ECE University of Minnesota
Thanks to Bell Museum.
Thanks to the Bryant-Lake Bowl.
Thanks to all of you for coming out. Friendly faces. OR
AND

2. Who is this guy? Most of the cells in his body are not his own!
Most of the cells in his body are not even human!
Most of the DNA in his body is alien!
Welcome to Cost-to-Coast AM with Art Bell.
“Minnesota Farmer”

3. Who is this guy? vs. He’s a human-bacteria hybrid:
[like all of us]
100 trillion bacterial cells of at least 500 different types inhabit his body.
vs.
only 1 trillion human cells of 210 different types.
“Minnesota Farmer”

4. Who is this guy? What’s in his gut? vs. He’s a human-bacteria hybrid:
[like all of us]
100 trillion bacterial cells of at least 500 different types inhabit his body.
vs.
What specifically is in his gut?
only 1 trillion human cells of 210 different types.
“Minnesota Farmer”

5. What’s in his gut?
“E. coli, a self-replicating object only a thousandth of a millimeter in size, can swim 35 diameters a second, taste simple chemicals in its environment, and decide whether life is getting better or worse.”
– Howard C. Berg
About 3 pounds of bacteria!
flagellum

6. Bacterial Motor

7. Bacterial Motor
Electron Microscopic Image

8. We should put these critters to work…
“Stimulus, response! Stimulus response! Don’t you ever think!”

9. Synthetic Biology Positioned as an engineering discipline.
“Novel functionality through design”.
Repositories of standardized parts.
Driven by experimental expertise in particular domains of biology.
Gene-regulation, signaling, metabolism, protein structures …

10. Building Bridges – Pam Silver, Harvard 2007 Engineering Design
"Think of how engineers build bridges. They design quantitative models to help them understand what sorts of pressure and weight the bridge can withstand, and then use these equations to improve the actual physical model. [In our work on memory in yeast cells] we really did the same thing.”
– Pam Silver, Harvard 2007
Engineering Design
Quantitative modeling.
Mathematical analysis.
Incremental and iterative design changes.

11. Building Digital Circuits
Intel 4004 (1971)
~2000 gates
Intel “Nehalem” (2008)
~2 billion gates

12. Building Digital Circuits
inputs
outputs
digital circuit … .
In my line of work, we are not designing programs to design digital circuits; we are design programs to design the programs to design the circuits (without begin facetious).
Design is driven by the input/output specification.
CAD tools are not part of the design process; they are the design process.

13. Synthetic Biology Feats of synthetic bio-engineering:
Cellulosic ethanol (Nancy Ho, Purdue, ’04)
Anti-malarial drugs (Jay Keasling, UC Berkeley, ‘06)
Tumor detection (Chris Voigt, UCSF ‘06)
Strategy: apply experimental expertise; formulate ad-hoc designs; perform extensive simulations.

14. From ad hoc to Systematic…
“A Symbolic Analysis of Relay and Switching Circuits,” M.S. Thesis, MIT, 1937
“A Mathematical Theory of Communication,” Bell System Technical Journal, 1948.
Claude E. Shannon 1916 –2001
Basis of all digital computation.
Basis of information theory, coding theory and all communication systems.

15. [computational] Analysis [computational] Synthetic Biology
“There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2004
Biological Process
Molecular Inputs
Molecular Products
Known / Unknown
Known
Unknown
Given
Unknown
Known

16. Going from reading genetic codes to writing them.
Artificial Life
Going from reading genetic codes to writing them.
US Patent (pending): “The present invention relates to a minimal set of protein-coding genes which provides the information required for replication of a free-living organism in a rich bacterial culture medium.”
– J. Craig Venter Institute

17. Going from reading genetic codes to write them.
Artificial Life
Going from reading genetic codes to write them.
Moderator: “Some people have accused you of playing God.”
J. Craig Venter: “Oh no, we’re not playing.

18. Biochemistry in a Nutshell
Nucleotides:
DNA: string of n nucleotides (n ≈ 109)
... ACCGTTGAATGACG...
Amino acid: coded by a sequence of 3 nucleotides.
Proteins: produced from a sequence of m amino acids (m ≈ 103).

19. The (nano) Structural Landscape
“You see things; and you say ‘Why?’ But I dream things that never were; and I say ‘Why not?’"
– George Bernard Shaw, 1925
Novel Materials…
Novel biological functions…
Heady Times. Novel Materials: Carbon nanotubes. Protein chemistry. Synthetic biology. (Yes, folks, synthetic life has arrived!)
Novel biochemistry…

20. “Now this end is called the thagomizer, after the late Thag Simmons.”
Jargon vs.Terminology
“Now this end is called the thagomizer, after the late Thag Simmons.”

21. The Computational Landscape
“There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
Semiconductors: exponentially smaller, faster, cheaper – forever?
Heady Times.
2000 transistors (Intel 4004, 1971)
800 million transistors (Intel Penryn, 2007)
1 transistor (1960’s)

22. The Computational Landscape
“There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
Semiconductors: exponentially smaller, faster, cheaper – forever?
Abutting true physical limits.
Cost and complexity are starting to overwhelm.
Heady Times.

23. The Computational Landscape
“There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
Potential Solutions:
Multiple cores?
Parallel Computing?
Intel blindsided the industry a few years ago with the announcement of multiple cores. The assumption: provide the hardware and the software will follow. Blue Gene: 64,000 processors. The history of computing is littered with sweeping predictions and attempts at implementing parallelization. Luminaries and startups (Thinking Machines, Cray Supercomputers). No one has really been able to crack the problem. Amhdals’ law.

24. The Computational Landscape
“There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
Potential Solutions:
Novel Materials? ? c
Provide the functionality and the “digital designers” will figure out how to use it. Novel function: apply computing in different ways.
Novel Function?

25. The Computational Landscape
“There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
RNAp
output
protein
gene

26. The Computational Landscape
“There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
RNAp
nada
repressor
protein
gene
Biological computation?

27. Research Activities in my Lab
Our research activities encompass topics in logic synthesis and verification, as well as in synthetic and computational biology. A broad theme is the application of expertise from the realm of circuit design to the analysis and synthesis of biological systems. Current projects include: ?
The concurrent logical and physical design of nanoscale digital circuitry.
The synthesis of stochastic logic for robust polynomial arithmetic.
Feedback in combinational circuits.
High-performance computing for the stochastic simulation of biochemical reactions.
The analysis and synthesis of stochasticity in biochemical systems.

29. Research Activities in my Lab
Circuits
We’re studying the mathematical functions for digital circuits.
We’re writing computer programs to automatically design such circuits.
Biology
We’re studying the concepts, mechanisms, and dynamics of intracellular biochemistry.
We’re writing computer programs for analyzing and synthesizing these dynamics.

31. [well, abstractions, really…]
Two Made-Up Facts
[well, abstractions, really…]
Logic Gates
Biochemical Reactions +

32. Logic Gates
“AND” gate 1
Generally thought of as acyclic structures (often defined as a DAG).
Once input values are applied, values propagate to the outputs.
Outputs assume definite boolean values regardless of the initial values on the wires and independently of all timing assumptions. 1

33. Logic Gates
“XOR” gate 1 1 1 1
Generally thought of as acyclic structures (often defined as a DAG).
Once input values are applied, values propagate to the outputs.
Outputs assume definite boolean values regardless of the initial values on the wires and independently of all timing assumptions. 1 1

34. Digital Circuit inputs outputs circuit
Realm of digital design is mature:
Combinational Circuit:
Circuit does not have any memory, or any internal state.
Performs a mapping from boolean inputs, to boolean outputs.

35. Digital Circuit inputs outputs gate circuit Combinational Circuit:
Circuit does not have any memory, or any internal state.
Performs a mapping from boolean inputs, to boolean outputs.

36. Digital Circuit 1 1 1 1 1 1 1 NAND OR AND NOR1 1
An acyclic circuit is clearly combinational.
If a circuit has no feedback paths resulting in cycles, that is to say if its topology is feed-forward, then its behavior is readily understood:
<click> When input values are applied, these propagate forward to the outputs.
The outcome is determine regardless of the prior values on the wires; clearly such a circuit does not preserve any state information so it is combinational. 1 1 1

37. My PhD Dissertation [yes, in one slide…]
An acyclic circuit is clearly combinational.
If a circuit has no feedback paths resulting in cycles, that is to say if its topology is feed-forward, then its behavior is readily understood:
<click> When input values are applied, these propagate forward to the outputs.
The outcome is determine regardless of the prior values on the wires; clearly such a circuit does not preserve any state information so it is combinational.

38. It’s not a bug, it’s a feature.

39. Current Research circuit Model defects, variations, uncertainty, etc.:
inputs
outputs
circuit 1
Characterize probability of outcomes.

40. Current Research circuit Model defects, variations, uncertainty, etc.:
inputs
outputs
p1 = Prob(one)
circuit
0,1,1,0,1,0,1,1,0,1,…
1,0,0,0,1,0,0,0,0,0,…
p2 = Prob(one)

41. Current Research circuit Model defects, variations, uncertainty, etc.:
inputs
outputs
circuit

43. Biochemical Reactions+
cell
protein
count 8 9 5 6 9 7

44. Biochemical Reactions
slow +
medium +
fast +

46. Bacteria are engineered to produce an anti-cancer drug:
Design Scenario
Bacteria are engineered to produce an anti-cancer drug:
triggering compound
drug
E. Coli

47. Bacteria invade the cancerous tissue:
Design Scenario
Bacteria invade the cancerous tissue:
cancerous tissue

48. Design Scenario Bacteria invade the cancerous tissue:
The trigger elicits the bacteria to produce the drug:
cancerous tissue

49. Design Scenario
Problem: patient receives too high of a dose of the drug.
The trigger elicits the bacteria produce the drug:
cancerous tissue

50. Conceptual design problem.
Design Scenario
Conceptual design problem.
Constraints:
Bacteria are all identical.
Population density is fixed.
Exposure to triggering compound is uniform.
No possible solution, it would seem. Bacteria are identical. Everything is fixed.
Requirement:
Control quantity of drug that is produced.

51. Approach: elicit a fractional response.
Design Scenario
Approach: elicit a fractional response.
cancerous tissue

52. Synthesizing Stochasticity
Approach: engineer a probabilistic response in each bacterium.
E. Coli
produce drug
with Prob. 0.3
triggering compound
Bacterial are identical by design. But this design could allow for a probabilistic response.
don’t produce drug
with Prob. 0.7

53. Engineering vs. Biology vs. Mathematics
Papa
Dilbert
Beaker

54. Communicating Ideas

55. Domains of Expertise Vision Language Abstract Reasoning Farming
Circuit
Number Crunching
Mining Data
Iterative Calculations
Human

56. “A person's mental activities are entirely due to the behavior of nerve cells, glial cells, and the atoms, ions, and molecules that make them up and influence them.”
– Francis Crick, 1982
Astonishing Hypothesis
“That the astonishing hypothesis is astonishing.”
– Christophe Koch, 1995
The Astonishing Part

57. Circuits & Computers as a Window into our Linguistic Brains
Conceives of circuits and computation by “applying” language. ?
Lousy at all the tasks that the brain that designed it is good at (including language).

58. If You Don’t Know the Answer…